Linguistic Structure Prediction (Synthesis Lectures on Human by Noah A. Smith, Graeme Hirst

By Noah A. Smith, Graeme Hirst

An enormous a part of normal language processing now will depend on using textual content facts to construct linguistic analyzers. We examine statistical, computational ways to modeling linguistic constitution. We search to unify throughout many techniques and plenty of forms of linguistic buildings. Assuming a easy realizing of traditional language processing and/or computer studying, we search to bridge the space among the 2 fields. methods to interpreting (i.e., conducting linguistic constitution prediction) and supervised and unsupervised studying of versions that expect discrete buildings as outputs are the focal point. We additionally survey normal language processing difficulties to which those equipment are being utilized, and we tackle similar themes in probabilistic inference, optimization, and experimental technique. desk of Contents: Representations and Linguistic info / deciphering: Making Predictions / studying constitution from Annotated information / studying constitution from Incomplete facts / past interpreting: Inference

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One word is the root of the tree, and each other word each has a single in-bound edge from its syntactic parent. In nonprojective parsing,10 any tree is allowed; in projective parsing, the trees are constrained so that all the words between any parent xi and child xj are descendents (reachable from) xi . Projective dependency parsing can also be represented as context-free parsing with a grammar whose nonterminals are annotated with words. 3. Phrase structures and dependency relationships can be united in a single framework through the use of lexicalized grammars.

26 2. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. t. 1). Only some features are shown; there are many more that could be nonzero for some y ∈ Yx . lc is the lower-casing function on strings. , pre 1 (Smith) = S). , shape(Smith) = Aaaaa). A gazetteer is a list of words or phrases known to refer to named entities; in this example, only Britain and English are in the gazetteer. 2. 3: Five views of linguistic structure prediction. Decoding means finding the best output y ∈ Yx (given input x), defined by (i) a mapping of “parts” π to R and (ii) a combination of the parts of y into a single score to be maximized.

30 2. 9) C∈C g(v ) where g is the “global” feature function obtained by summing the feature functions at all the cliques. 8 Note that we can think of gj (V ) as a random variable; it is a deterministic function from assignments of V (which are random) to R. 1. 7 We will return to log-linear models at length in chapter 3. For now, the simple explanation of the term is that logarithm of the clique potential value (or of the probability) corresponds to a linear function of the feature vector representation.

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